Learning analytics and Medical Education
Abstract
The number of courses delivered online or supported using learning management systems continues to rise; both in size and number, it is used by more students and employees to gain new knowledge, learn skills and earn degrees. (1)
    In contrast to traditional methods of teaching that leave little trail behind, online learning has the potential to generate huge amounts of data about the students and their way of learning in terms of records, logs, interactions and digital footprint. (2)
    The availability of huge datasets, increased computer power and the pressure towards better teaching and learning, personalization of the content and improving educational system has led to increased interest in learning analytics research. (3, 4, 5)
    Learning analytics (LA) is an emerging and rapidly developing research discipline in the field of technology enhanced learning that -by definition- aims at “measurement, collection, analysis and reporting of these data and their contexts, for purposes of understanding and optimizing learning and the environments in which it occursâ€. (2, 5)
    Using analytics in the field of higher education can improve the decision making process based on actual data and real trends that are derived from students’ behavior and resource usage. LA can foster institutional growth, increase productivity, create innovative models and enable institutions to understand strengths and challenge. (5, 6) It can be used to predict students’ performance, personalize instruction, build adaptive systems and alarms of potential underachievement in real time. (3, 6, 7)
    The future holds promise in bringing more sources of data that can add to the pool; from smartphones and smart watches, wearable technologies, Internet of Things (IoT), tracking devices, biometrics, sensors, smart medical training devices with logging modules, from RFID-Based attendance and beacon devices.
    There is also a venue of a new category of smart and networked hardware; An example could be a microscope that records all data specific to students’ usage, these data will be later analyzed to better understand and identify the patterns that are best linked to effective training which can be used to create adaptive systems. This is will also be possible to stethoscopes, sphygmomanometers as well as many other tools.
    Unlike other sectors that has made great strides in the field of analytics, the education sector is late in reaping the benefits and harnessing the developments of data science, and it is even worse in the case of medical education where research is very limited in number and quality. A fact that necessitates the development of skills in education analytics and data science, infrastructure to support it, modifications to the existing learning management systems and new tools designed for this specific task.Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).